Literatura académica sobre el tema "Statistique CUSUM"
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Artículos de revistas sobre el tema "Statistique CUSUM"
Désilets, L., C. Langlois, A. Lamarche y D. CLuis. "Tendances temporelles de la qualité physico-chimique de l’eau du fleuve Saint-Laurent (Tronçon Cornwall-Quebec) au cours de la période 1955 a 1986". Water Quality Research Journal 23, n.º 4 (1 de noviembre de 1988): 542–55. http://dx.doi.org/10.2166/wqrj.1988.042.
Texto completoTesis sobre el tema "Statistique CUSUM"
Sahki, Nassim. "Méthodologie data-driven de détection séquentielle de ruptures pour des signaux physiologiques". Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0185.
Texto completoThis thesis deals the problem of change-point detection in the sequential framework where the signal is assumed to be observed in real time and the phenomenon changes from its "normal" starting state to an "abnormal" post-change state. The challenge of sequential detection is to minimize the detection delay, subject to a tolerable false alarm limit. The idea is to sequentially test for the existence of a change-point by recursively writing the detection statistic as a function of the score, which replaces the Log-Likelihood Ratio when the data distribution is unknown. The detection procedure is thus based on a recursive statistic, a detection threshold and a stopping rule. In a first work, we consider the score-CUSUM statistic and propose to evaluate the detection performance of some detection thresholds. Two thresholds come from the literature, and three new thresholds are constructed by a method based on simulation: the first is constant, the second instantaneous and the third is a dynamic "data-driven" version of the previous one. We rigorously define each of the thresholds by highlighting the different notions of the controlled false alarm risk according to the threshold. Moreover, we propose a new corrected stopping rule to reduce the false alarm rate. We then perform a simulation study to compare the different thresholds and evaluate the corrected stopping rule. We find that the conditional empirical threshold is the best to minimize the detection delay while maintaining the tolerated risk of false alarms. However, on real data, we recommend using the data-driven threshold as it is the easiest to build and use for practical implementation. In the second part, we apply our data-driven detection methodology to physiological signals, namely temporal signals recorded at the level of the upper trapezium beam of 30 subjects performing different office activities. The methodology is subject-activity dependent; it includes the on-line estimation of the signal parameters and the construction of the data-driven threshold on the start of the signal of each activity of each subject. The objective was to identify regime changes during an activity in order to assess the level of muscle solicitation and EMG signal variability, which are associated with muscle fatigue. The results obtained confirmed the ease of our methodology and the performance and practicality of the proposed data-driven threshold. Subsequently, the results allowed the characterization of each type of activity using mixed models
Verdier, Ghislain. "Détection statistique de rupture de modèle dans les systèmes dynamiques : application à la supervision de procédés de dépollution biologique". Phd thesis, Montpellier 2, 2007. http://www.theses.fr/2007MON20200.
Texto completoThis thesis considers the problem of model change detection in complex dynamic systems. The goal is to develop statistical methods able to detect possible change of parameters in the model describing the system, while keeping a low rate of false alarms. This type of method is applied to the detection of anomaly or failure in many systems (navigation system, quality control. . . ). The methods developed take into account the characteristics of biotechnological processes, which are the main application of this work. Thus, the development of a CUSUM type procedure, based on estimation of conditional likelihoods enable to treat, first, the case where a part of the model is unknown by using a nonparametric approach to estimate this component, and second, the case frequently met in practice where the system is observed indirectly. In this second case, approaches such as particle filtering are used. Several results of optimality under classical constraints are established for the proposed approaches which are applied to a real problem, a bioreactor for wastewater treatment
Verdier, Ghislain. "Détection Statistique de Rupture de Modèle dans les Systèmes Dynamiques - Application à la Supervision de Procédés de Dépollution Biologique". Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2007. http://tel.archives-ouvertes.fr/tel-00221418.
Texto completoLes méthodes développées ici prennent en compte les caractéristiques des procédés de dépollution biologique, qui constituent l'application principale de ce travail. Ainsi, la mise au point d'une procédure, de type CUSUM, construite à partir des estimations des vraisemblances conditionnelles permet de traiter, d'une part, le cas où une partie du modèle est inconnue en utilisant une approche non paramétrique pour estimer cette partie, et d'autre part, le cas fréquemment rencontré en pratique où le système est observé indirectement. Pour ce deuxième cas, des approches de type filtrage particulaire sont utilisées.
Des résultats d'optimalité sont établies pour les approches proposées. Ces approches sont ensuite appliquées à un problème réel, un bioréacteur de retraitement des eaux usées.
Charette, Kevin. "Différents procédés statistiques pour détecter la non-stationnarité dans les séries de précipitation". Thèse, 2014. http://hdl.handle.net/1866/10637.
Texto completoThe main goal of this master's thesis is to find whether the summer convective precipitations simulated by the Canadian Regional Climate Model (CRCM) are stationary over time or not. In order to answer that question, we propose both a frequentist and Bayesian statistical methodology. For the frequentist approach, we used standard quality control and the CUSUM to determine if the mean has increased over the years. For the Bayesian approach, we compared the posterior distributions of the precipitations over time. In order to do the comparison, we used a statistic based on the Hellinger's distance, the J-divergence and the L2 norm. In this master's thesis, we used the ARL (average run length) to calibrate each of our methods. Therefore, a big part of this thesis is about studying the actual property of the ARL. Once our tools are well calibrated, we used the simulation to compare them together. Finally, we studied the data from the CRCM to decide, whether or not, the data are stationary.